Correlative, ML based and non destructive 3D analysis of intergranular fatigue cracking in SAC305 Bi solder balls

Abstract

Reliable connections of electrical components embody a crucial topic in the microelectronics and power semiconductor industry. This study utilises 3D nondestructive Xray tomography and specifically developed machine learning (ML) algorithms to statistically investigate crack initiation and propagation in SAC305Bi solder balls upon thermal cycling on board (TCoB). We quantitatively segment fatigue cracks and flux pores from 3D Xray tomography data utilising a multilevel MLworkflow incorporating a 3D U-Net model. The data reveals that intergranular fatigue cracking is the predominant failure mechanism during TCoB and that dynamic recrystallisation precedes crack initiation. Moreover, we find that fatigue cracks are initiated at surface notches, flux pores and printed circuit boardmetallisation intrusions. The work provides important insights regarding the underlying microstructural and mechanical mechanisms for recrystallisation and cracking, uniting the aspects of bigdata analysis with MLalgorithms and indepth understanding about the underlying materials science.